Methods and evaluation devices for analyzing three-dimensional data sets representing devices

ABSTRACT

Methods and evaluation devices for evaluating 3D data of a device under inspection are provided. A first machine learning logic detects target objects, and a second machine learning logic provides a voxel segmentation for the target objects. Based on the segmented voxels, a transformation to feature space is performed to obtain measurement results.

TECHNICAL FIELD

The present application relates to methods and devices for analyzingthree-dimensional (3D) data sets representing a device under inspection,in particular target objects like three-dimensional structures as foundin semiconductor devices. Such methods and devices can for example beused to detect anomalies, faults or the like in the target objects orfor measuring the target objects.

BACKGROUND

In the manufacture of semiconductor devices, manufactured devices aremonitored during or after production or also as test samples whensetting up a production line. With increasing miniaturization, suchsemiconductor devices include more and more tightly packed structures,which presents a challenge for evaluation. For example, nowadays complexsemiconductor devices may include two or more chip dies stacked one onthe other, with a high number of metal interconnects embedded ininsulating materials (dielectric material or also air or another gas)provided for electrical connections. Faults in these interconnects mayadversely affect the functioning of the device, sometimes only afterlonger use and therefore not immediately apparent by performing anelectrical function test. Such structures like interconnects which areembedded in another material are also referred to as embedded 3Dstructures herein.

Various techniques exist for obtaining 3D data sets for such embeddedstructure, including optical methods, X-ray methods, scanning electronmicroscopy (SEM)-based methods and computer tomography (CT) microscopy,some of which involve destroying the device (for example by removinglayer by layer from the device and obtaining a 2D image from each layerbefore it is removed), and some being non-destructive like CTmicroscopy.

For structures found in semiconductor devices, which have smalldimensions and may for example include a high number of structures likeinterconnects between chip dies, the amount of 3D data resulting islarge. Therefore, efficient methods for analyzing this data are needed.

While classical computer vision techniques and machine learningtechniques have been applied to this problem, current solutions sufferfrom various drawbacks like the need for defining measurement templatesthat are manually or automatically fit to a device data, the need forspecific measurement recipes that provide procedural instructions forsearching and detection of structural features such as edges andcorners, manual data registration and limited generalizability. Thesedrawbacks can become more severe as the number of structures in a deviceunder inspection increases, or more structural and topographicalvariations are encountered.

SUMMARY

Methods and devices as defined in the independent claims are provided.The dependent claims define further embodiments.

According to an embodiment, a method for evaluating 3D data of a deviceunder inspection, comprising:

detecting target objects in the 3D data using a first machine learninglogic,

applying a voxel classification to the detected target objects using asecond machine learning logic to provide a segmentation of voxelsdepending on a material of the device the respective voxel represents,

applying a transformation to feature space to the classified voxels, and

obtaining measurement results based on the transformation to featurespace.

Through the combination of the first and second machine learning logicwith a transformation, efficient processing of the 3D data can beachieved.

The term target object relates to any three-dimensional (3D) structureof the device. Such 3D structures may be repetitive, meaning that withina device a plurality of similar structures are provided.

The term machine learning logic refers to an entity that can be trainedby training data to be able to perform certain tasks, in the context ofthe present application segmentation tasks as will be explained furtherbelow. A machine learning logic can for example be based on neuralnetworks like deep neural networks, general adversarial networks,convolution neural networks or support vector machine, but can alsoinclude approaches like random forest models like random hough forestmodels or 3D random forest models or decision trees. Machine learninglogics are implemented on electrical devices like computers. Allreferences to such electrical devices and the functionality provided byeach are not intended to be limited to encompassing only what isillustrated and described herein. While particular labels may beassigned to such electrical devices disclose, such levels are notintended to limit the scope of operation for the electrical devices.Such electrical devices can be combined with each other and/or separatedin any manner based on the particular type of electrical implementationthat is desired. For example, various functions can be performed indifferent devices connected via a network. It is recognized thatelectrical devices disclosed herein that are usable for implementingtechniques discussed herein can include any number of microcontrollers,machine learning specific hardware, for example a graphics processorunit (GPU), and/or a tensor processing (TPU), integrated circuits,memory devices (for example flash, random access memory, read-onlymemory, electrically programmable read-only memory, electricallyerasable programmable read only memory or other suitable variancethereof), and software which co-act with one another to performoperation(s) disclosed herein. In addition, any one or more of theelectrical devices can be configured to execute a program code that isembodied in a non-transitory computer readable medium, a data carriersignal or the like program to perform any number of the functions asdiscussed herein.

The term voxel is derived from the words volume and elements and incomputer graphics represents a value on a regular grid inthree-dimensional space, for example a color value like RGB value,grayscale value, intensity value or the like.

The term feature space generally relates to features that are used tocharacterize the 3D data.

The device may be a semiconductor device. Semiconductor devices includeone or more semiconductor chip dies, and may also include furthercomponents like interconnects between chip dies in case a plurality ofchip dies are provided, interconnects like bond wires to externalterminals of the semiconductor device, for example pins of a package,the package itself, etc.

In this case, the target objects may be interconnects between chip dies.

The first machine learning logic can comprises a hough forest model. Thesecond machine learning logic can comprise a 3D random forestsegmentation model. However, other types of machine learning logic canalso be used.

The transformation to feature space can include a transformation tolinear feature space.

The transformation to feature space can comprise providing one or morefunctions describing a dependency of a first dimensional variable to asecond dimensional variable, or derivatives thereof. A dimensionalvariable is to be understood as a variable describing dimensions likeheight, diameter, area or volume of the target objects.

The first dimensional variable can include an area or a diameter, andthe second dimensional variable can include a position variable likeposition in length or depth direction, such that, e.g., area or diametercan be given as a function of depth or length position.

Obtaining measurements can include identifying deviations of thefunctions from nominal functions, i.e. functions expected if the targetobjects are essentially as designed, within acceptable tolerances.

The one or more functions are user configurable. This in someembodiments can allow flexibility regarding the measurements.

In some embodiments, a predictive model can be used to predict a desiredconfiguration of a user. In this way, the number of manualconfigurations a user needs to make can be reduced.

A corresponding evaluation device for evaluating 3D data of a deviceunder inspection is also provided, comprising one or more processorsconfigured to:

detect target objects in the 3D data using a first machine learninglogic,

apply a voxel classification to the detected target objects using asecond machine learning logic to provide a segmentation of voxelsdepending on material of the device the respective voxel represents,

apply a transformation to feature space to the classified voxels, and

obtain measurement results based on the transformation to feature space.

The evaluation device can be configured to execute any of the methodsabove.

A system, comprising a measurement device configured to obtain 3D dataof a device under test, and the above evaluation device is alsoprovided.

Furthermore, a method for training the evaluation device is provided,comprising:

training the first machine learning logic based on training data withannotated target objects, and

training the second machine learning logic with training data includingannotated voxels.

Corresponding computer programs and tangible storage media storing thecomputer program (e.g., CD, DVD, flash memory, read only memory, etc.)are also provided.

DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating a system according to anembodiment.

FIG. 2 is a flowchart illustrating a method according to an embodiment.

FIG. 3 is a flowchart illustrating a method according to a furtherembodiment.

FIG. 4 is a diagram illustrating a method according to an embodiment.

FIGS. 5 to 8 show example structures for illustrating embodiments.

FIGS. 9 and 10 illustrate measurement results for example structures forfurther illustration.

FIGS. 11A to 11E show examples for illustrating the method of FIG. 3 .

FIG. 12 is an implementation example of a predictive model according tosome embodiments.

DETAILED DESCRIPTION

In the following, various embodiments will be discussed in detailreferring to the attached drawings. It is to be understood that theseembodiments serve as example only and are not to be construed aslimiting. For example, while embodiments may be described including aplurality of features (elements, components, acts, events, method stepsand the like), in other embodiments some of these features can beomitted and/or can be replaced by alternative features. In addition tothe features explicitly shown and described, in other embodimentsadditional features, for example features conventionally used foranalyzing three-dimensional structures in devices and systems likesemiconductor devices, can be provided.

Features from different embodiments can be combined unless notedotherwise. Modifications and variations described with respect to one ofthe embodiments shown and described can also be applied to otherembodiments and will therefore not be described repeatedly.

Semiconductor devices, in particular interconnects between chip dies,will be used as example for 3D structures as target objects herein. Itis to be understood that techniques discussed herein can also be appliedto other 3D structures, in particular embedded 3D structures.

Turning now to the Figures, FIG. 1 is a block diagram of a system 10according to an embodiment, including an evaluation device 12 accordingto an embodiment.

System 10 includes a measurement device 11 configured to obtainthree-dimensional data from a device under inspection 13. Device underinspection 13 may for example be a semiconductor device, for example asemiconductor device including a plurality of chips stacked on eachother and interconnects between the chips. Measurement device 11 can beany measurement device conventionally used to obtain 3D data from device13 in a destructive or nondestructive manner. For example, measurementdevice 11 can be based on optical approaches, x-ray approaches orscanning electron microscopy or computer tomography (CT) microscopy. Asan example for a destructive approach, device under inspection 13 may beremoved layer by layer, and for each layer a scanning electronmicroscopy image can be obtained, such that all images together form a3D representation of device under inspection 13. As an example for anondestructive method, computer tomography microscopy can be used. Suchtechniques for obtaining three-dimensional data are for example furtherdescribed in M. Kaestner, S. Mueller, T. Gregorich, C. Hartfield, C.Nolen and I. Schulmeyer, “Novel Workflow for High-Resolution Imaging ofStructures in Advanced 3D and Fan-Out Packages,” 2019 ChinaSemiconductor Technology International Conference (CSTIC), 2019, pp.1-3, doi: 10.1109/CSTIC.2019.8755668, in Li, Y., Hatch, O., Liu, P. etal. Root Cause Investigation of Lead-Free Solder Joint InterfacialFailures After Multiple Reflows, Journal of Electronic Materials 46,1674-1682 (2017). https://doi.org/10.1007/s11664-016-5211-0, in C.Schmidt, S. T. Kelly, Y. Wang, S. T. Coyle and M. H. Shearer, “Novelsample preparation and high-resolution X-ray tomography for package FA,”2017 IEEE 24th International Symposium on the Physical and FailureAnalysis of Integrated Circuits (IPFA), 2017, pp. 1-4, doi:10.1109/IPFA.2017.8060174 or in C. Schmidt, “X-ray Imaging Tools forElectronic Device Failure Analysis,” Microelectronics Failure AnalysisDesk Reference, Seventh Edition, 2019, pp. 62-66.

The thus obtained 3D data of device under inspection 13 is then providedto evaluation device 12 for evaluation. It should be noted thatevaluation device 12 can be located remote from measurement device 11,and the 3D data can be transferred to evaluation device 12 over anetwork like a local area network (LAN), wireless network, for exampleWLAN, or over the internet.

Evaluation device 12 can be a computing device like a computer,microcontroller or other programmable processing device programmed toperform the analysis discussed herein below referring to FIGS. 2 to 10 .Evaluation device 12 can include one or more processors. In someinstances, evaluation device 12 need not be provided as a single unitarydevice, but can also be provided as a plurality of devices connected viaa network, such that different stages of the analysis described belowcan be performed in different devices. Generally, as will be describedin more detail, evaluation device 12 utilizes a two-step machinelearning approach to classify voxels of the 3D data, and then usetransformations on the classified voxel to a feature space. In otherembodiments, evaluation device 12 can be fully or partially implementedusing dedicated hardware, like application-specific integrated circuits(ASICs), to implement techniques as discussed herein.

FIG. 2 is a flowchart illustrating a method according to an embodiment.The method of FIG. 2 can be implemented in evaluation device 12 of FIG.1 , for example using one or more corresponding computer programs toprogram a microprocessor, central processing unit, graphics processingunit or other suitable processor accordingly.

At 20, the method of FIG. 2 comprises detecting target objects using afirst machine learning logic. The term “target objects” relate toobjects, in the present case three-dimensional objects or structures,which are to be evaluated by evaluation device 12. For example, in caseof a semiconductor device, the target objects can be interconnectsbetween chips, structures in or on chips, connections from chips to pinsof a package, etc.

At 21, the method includes applying a voxel classification to thedetected target objects using a second machine learning logic. Voxelclassification means that the voxels of the detected target objects areclassified for example based on different materials the respective voxelrepresents. For example, in case of interconnects, different materialsmay include solder material, copper leads, tungsten, surroundingdielectric material, etc.

The first and second machine learning logics can be trained beforehand.A corresponding method is shown in FIG. 3 . The method of FIG. 3 will befurther explained referring to FIGS. 11A to 11E.

At 30, the method of FIG. 3 includes training the first machine learninglogic used at 20 in FIG. 2 . For this, training sets can be provided,for example 3D data sets obtained by measurement device 11 where thetarget objects are for example manually annotated, i.e. identified, orannotated by other techniques like image recognition techniques notusing a machine learning logic.

As an illustrative, non-limiting example, FIGS. 11A and 11B show variousviews of example objects of a chip on chip interconnect structure. Atarget object 1102, shown from different perspectives, is annotatedmanually by a bounding box 1101 in a graphical user interface (GUI) andmarked by a marker 1103 (symbolized by a “+”) as a target object.Furthermore, negative annotations can be used, as shown in FIG. 11B.Here, an object 1105, which is not a target object to be analyzed, ismarked by a bounding box 1104 and marked with a marker 1106 (symbolizedby a “−”) as a non-target object. Positive annotations of target objectsas in FIG. 11A can increase the true positive rate of the trained firstmachine learning logic, i.e. the amount of target objects successfullydetected. Negative annotations of non-target objects as in FIG. 11B candecrease the false positive rate of the trained first machine learninglogic, i.e. the amount of non-target objects identified as targetobjects.

These annotated objects are then used to train the firstmachine-learning logic. The trained machine learning logic can then beused to process a 3D training data volume. FIG. 11C shows an exampleresult, where target objects 1108 and non-target objects 1107 have beenidentified by the trained first machine-learning logic. To improve theobject detection by the first machine learning logic, a human operatormay correct these identifications if necessary (e.g., mark wronglydetected target objects with marker 1106 and mark not detected targetobjects with marker 1103 explained with reference to FIGS. 11A and 11B)and retrain the first machine-learning logic based on these corrections.

After training the first machine learning logic, at 31, the method ofFIG. 3 includes training the second machine learning logic. Here, theannotated 3D data sets used for training the machine learning logic canbe further annotated in that the different voxels are identified andclassified for example manually. In other embodiments, a separatetraining set can be used. For this, after 30, 3D data sets can beprocessed with the first machine learning logic as at 20 in FIG. 2 , andthen the detected target objects may be manually annotated to classifythe voxels. In both cases, the data with annotated classified voxels canthen be used for training the second machine learning logic.

As a non-limiting example, as shown in FIG. 11D, a subset of the targetobjects identified by the trained first machine-learning logic asillustrated in FIG. 11C can be further annotated. In the example of FIG.11D, a background 1110 and solder material 1109 is annotated. Othermaterials (e.g., tungsten) may be annotated as well. Then, thesecond-machine-learning logic is trained based on these annotations.FIG. 11E shows an example result of applying the trained secondmachine-learning logic to training data. As for the firstmachine-learning logic, corrections can be made and re-training can beperformed.

Once the training is completed, the thus trained first and secondmachine-learning logic are ready to be used.

Returning now to FIG. 2 , the method continues with applying atransformation to the classified voxels. The transformation cantransform the classified voxel to a feature space, for example linearfeature space, to provide functions of features of the target objectslike cross-sectional area profile along a direction thereof, derivativethereof, or other dimensions of the target objects. An example for sucha transformation is described in Kanyiri, C. W., Kinyanjui, M. &Giterere, K., “Analysis of flow parameters of a Newtonian fluid througha cylindrical collapsible tube,” SpringerPlus 3, 566 (2014).

Finally, from the transformations at 23 measurement results, for exampleof the target objects or information regarding faults of the targetobjects can be obtained.

FIG. 4 is a diagram illustrating an example workflow for analyzing 3Ddata sets 40, which can for example be obtained by measurement device 11of FIG. 1 for a device under inspection. The workflow of FIG. 4 is animplementation example of the methods of FIGS. 2 and 3 . The variousblocks as shown in FIG. 4 can be implemented as software components onevaluation device 12. In some embodiments, with the workflow of FIG. 4 asolution with high throughput and functionality combined with acomparatively low training effort can be obtained.

The workflow of FIG. 4 will be further explained referring to FIGS. 5 to10 using interconnects between chips as an example for target objects,similar to FIGS. 11A-11E above. It is to be understood that this is onlyfor illustration purposes, and in other embodiments other objects may beused.

FIG. 5 , as an example for 3D data sets 40, shows a 3D rendering of anintegrated circuit package sample in an image part 50 with a pluralityof interconnects 53 between chip dies. An image part 52 shows a top viewof the interconnects, and image parts 51 and 54 show cross sectionalviews thereof. Interconnects 53, as mentioned above, here serves astarget objects to be analyzed. As can be seen, a chirp may comprise ahigh number of such interconnects as three-dimensional structures,making an automated analysis and evaluation of these structuresdesirable. In the example of FIG. 5 , the 3D data was obtained bycomputer tomography microscopy.

In some embodiments, the 3D data set can be first subjected toconventional computer vision preprocessing 413 like filtering, noisereduction or sharpening.

The 3D data 40 is subjected to object detection 45 by a first machinelearning logic. The first machine learning logic can be a random houghforest 3D object detector. In a training phase indicated by a box 41(see also explanations to FIGS. 3 and 11A-11E), training data 42 isprovided with training annotations 43 to provide an inference modellibrary 44 for training the first machine learning logic and also thesecond machine learning logic mentioned below. During the actualanalysis, the object detection 45 can provide object detection for thetarget structures like interconnects it was trained for.

FIG. 6 shows an example for the result of such an object detection,where target interconnects 60 are marked with a “+” signs and have boxessurrounding them, which mark the identified regions with the targetobjects. In the 3D case, the boxes can be three-dimensional boxes, andthe two-dimensional representation of FIG. 6 is only for illustrationpurposes, see also the explanations for FIGS. 11A and 11B above.

Next, returning to FIG. 4 , at 46 a voxel classification is performed bya second machine learning logic. Here, different voxels are attributedto different materials, for example, in case of interconnects, copper,solder material, tungsten or surrounding dielectric material. Thetraining of the second machine learning logic (31 in FIG. 3 ) can againbe performed based on the inference model library 44 obtained byannotating training data, as explained with reference to FIG. 3 .

Classifying voxels may also be referred to as segmentation, and singleclass segmentation (simply separating the actual interconnect materialfrom the surrounding material in the example) or multi classsegmentation (distinguishing different materials in the example ofinterconnects) can be used. FIG. 7 illustrates an example where some ofthe interconnects identified in FIG. 6 have been subjected tosegmentation, such that the exact contours of the interconnect materialin voxel space (i.e. voxels for example belonging to solder and voxelsseparated from voxels corresponding to surrounding material) arevisible.

Next, at 47 in FIG. 4 , the classified voxels are transformed to featurespace, for example linear feature space. In other words, properties ofthe target objects are determined as functions depending on a linearparameter. Other non-linear or multi-variate transformations can be usedas well. It should be noted that a single feature transform or alsomultiple feature transforms can be performed.

The following are examples of various transforms that could be performedon classified voxel data volume: (1) count of voxel whose values liebetween a specified range, (2) object volumes based on values between aspecified range, (3) object bounding box dimensions, (4) objectcentroids, and (5) object major/minor axes.

In the embodiment shown in FIG. 8 , the sub-volumes containing detectedobjects are transformed onto a centroid line along the principal axes ofeach object (denoted by transform T). At each point along the centralaxis, a cross-sectional area orthogonal to the axis is computed, therebytransforming each detected object (O_(n)) into an area (A_(c))parameterized by the linear position (s) along the central axis:

T: O_(n)->A_(c)(s), where s represents the linear distance from theorigin of the central axis

This transform can be computed by extracting the bounding contours ofthe binarized thresholded image object within a sequence ofcross-sectional images. Similar transforms for biomechanical modelinghave described in Mahmoudi, Moeinoddin, Dorali, Mohammad Reza & Beni,Mohsen, Mahbadi, Hossein, ISME 2018, “Bio-CAD modeling of femoral boneswith Dual X-ray absorptiometry and Spiral CT-scan technique.” Thesecontours can be obtained using a standard contour detection algorithm asdescribed in Bradski, G., Kaehler, A. (2008), “Learning OpenCV: Computervision with the OpenCV library,” O'Reilly Media, Inc., pp. 144-189. Thecentral axis of the object can be determined by computing the centroidsof each cross-sectional binarized image and obtaining the best fit linethrough all the centroids. Other standard transformations can also befound in Bradski, G., Kaehler, A. (2008), “Learning OpenCV: Computervision with the OpenCV library.”

Several functions 48_1 to 48_N of FIG. 4 , also labeled Φ₁ to Φ_(N) canbe produced as a result of the feature transforms. As an example, asindicated by arrows 80, cross-sectional area profile along the height ofeach isolated interconnect can be calculated as a function of the height(or depth), and a derivative of the cross-sectional area as a functionof height can be calculated. Other examples for functions 48 includediameter or also further derivatives (second derivative, thirdderivative, etc.) The functions can include user tunable parameters 49.For example, the user tunable parameters can include quantities for thefunctions, i.e. quantities that modify the way that objects are measured(like diameter, area, height, etc.). These can include parameters suchas threshold values for edge detection or image binarization, weightsfor Laplacian and Gaussian filtering operations, and radii formorphological operators such as erosion and dilation, to mention a few.Some examples can also be found in Bradski, G. and Kaehler, A. citedabove.

Based on the selections made by the user for user tunable parameters 49,in some embodiments an optional predictive model 412 can be used topredict which parameters a user will likely use and set them in advance,so the required input by the user is reduced.

Predictive model 412 in embodiments constructs a cumulative trainingdataset that can comprise, but is not limited to, sample dataset images,sample object detections, and sample voxel classes along with userspecified control parameters such as trace selections, edge countselections, edge thresholds, etc. FIG. 12 illustrates an exampleimplementation of predictive model 412. For each new dataset that isprocessed by the end user in the workflow (e.g., sample slices, classesor objects for which the end user determines user parameters), thisdataset can be used as a training dataset 1201 for a machine learningmodel such as a convolution neural network (CNN) 1202, that can betrained to predict the values of the user specified control parameters,i.e. training such that an estimation error 1203 is minimized.

The CNN then predicts (or estimates) the control parameters based solelyon the sample inputs and these predictions are compared against the userspecified control parameters. Once the estimation error consistentlyfalls below a pre-defined threshold (for example, less than 5%), thepredicted values can then be presented to the user as recommendedsettings for the control parameters. The behavior of the predictivemodel is thus very similar to a standard machine learning basedrecommender system as described in Aggarwal C. C. (2016), “AnIntroduction to Recommender Systems,” Recommender Systems, Springer,Cham. https://doi.org/10.1007/978-3-319-29659-3_1.

From these functions 48, at 410 measurements and detections of faultconditions can be performed. The results can be output as reports 411,e.g., in data files, can be displayed graphically on a display, or both.For example, FIG. 9 shows an example for an interconnect from a start ofa copper (Cu) pad 82 to a bottom of a copper shoulder 81 shown in FIG. 8and beyond.

A curve 93 shows the cross-sectional area, and curves 90 to 92 showderivatives. “BLT candidate measurement” indicates a distance from thestart of the copper pad to the bottom of the copper shoulder.

FIG. 10 shows a corresponding measurement where a void 1004 is presentin the interconnect. As can be seen, the presence of the void is clearlyvisible in curves 1000 to 1003, showing the same quantities as curves 90to 93, respectively. Therefore, by analyzing the curves defects likevoids can be found. For example, the curves resulting of the measurementfunctions can be compared to nominal curves or curves obtained fromfault free interconnects, and deviations above some threshold can beindicative of an error.

Therefore, with the approach illustrated, even high volumes of data, forexample a plurality of interconnects, can be efficiently measured.

Some embodiments are defined by the following examples:

Example 1. A method for evaluating 3D data of a device under inspection,comprising:

detecting target objects in the 3D data using a first machine learninglogic,

applying a voxel classification to the detected target objects using asecond machine learning logic to provide a segmentation of voxelsdepending on a material of the device the respective voxel represents,

applying a transformation to feature space to the classified voxels, and

obtaining measurement results based on the transformation to featurespace.

Example 2. The method of example 1, wherein the device is asemiconductor device.

Example 3. The method of example 2, wherein the target objects areinterconnects between chip dies.

Example 4. The method of any one of examples 1 to 3, wherein the firstmachine learning logic comprises a hough forest model.

Example 5. The method of any one of examples 1 to 4, wherein the secondmachine learning logic comprises a 3D random forest segmentation model.

Example 6. The method of any one of examples 1 to 5, wherein thetransformation to feature space includes a transformation to linearfeature space.

Example 7. The method of any one of examples 1 to 6, wherein thetransformation to feature space comprises providing one or morefunctions describing a dependency of a first dimensional variable to asecond dimensional variable, or derivatives thereof.

Example 8. The method of example 7, wherein the first dimensionalvariable includes an area or a diameter.

Example 9. The method of examples 7 or 8, wherein the second dimensionalvariable includes a position variable.

Example 10. The method of any one of examples 7 to 9, wherein obtainingmeasurements includes identifying deviations of the functions fromnominal functions.

Example 11. The method of any one of examples 7 to 10, wherein the oneor more functions are user configurable.

Example 12. The method of example 11, furthermore comprising predictinga desired user configuration.

Example 13. An evaluation device for evaluating 3D data of a deviceunder inspection, comprising one or more processors configured to:

detect target objects in the 3D data using a first machine learninglogic,

apply a voxel classification to the detected target objects using asecond machine learning logic to provide a segmentation of voxelsdepending on material of the device the respective voxel represents,

apply a transformation to feature space to the classified voxels, and

obtain measurement results based on the transformation to feature space.

Example 14. The evaluation device of example 13, wherein the deviceunder inspection is a semiconductor device.

Example 15. The evaluation device of example 14, wherein the targetobjects are interconnects between chips.

Example 16. The evaluation device of any one of examples 13 to 15,wherein the first machine learning logic comprises a hough forest model.

Example 17. The evaluation device of any one of examples 13 to 16,wherein the second machine learning logic comprises a 3D random forestsegmentation model.

Example 18. The evaluation device of any one of examples 13 to 17,wherein the transformation to feature space includes a transformation tolinear feature space.

Example 19. The evaluation device of any one of examples 13 to 18,wherein for the transformation to feature space the one or moreprocessors are configured to provide one or more functions describing adependency of a first dimensional variable to a second dimensionalvariable, or derivatives thereof.

Example 20. The evaluation device of example 19, wherein the firstdimensional variable includes an area or a diameter.

Example 21. The evaluation device of examples 19 or 20, wherein thesecond dimensional variable includes a position variable.

Example 22. The evaluation device of any one of examples 19 to 21,wherein for obtaining measurements the one or more processors areconfigured to identify deviations of the functions from nominalfunctions.

Example 23. The evaluation device of any one of examples 19 to 22,wherein the one or more functions are user configurable.

Example 24. The evaluation device of example 23, furthermore comprisinga predictive model configured to predict a desired user configuration.

Example 25. A system, comprising:

a measurement device configured to obtain 3D data of a device undertest, and

the evaluation device of any one of examples 13 to 24.

Example 26. A method for training the evaluation device of any one ofexamples 13 to 25, comprising:

training the first machine learning logic based on training data withannotated target objects, and

training the second machine learning logic with training data includingannotated voxels.

Example 27. A computer program including a program code which, whenexecuted on a processor, causes execution of the method of any one ofexamples 1 to 12.

Example 28. A tangible non-transitory storage medium having the computerprogram of example 27 stored thereon.

These examples are not to be construed as limiting.

What is claimed is:
 1. A method for evaluating 3D data of a device underinspection, comprising: detecting target objects in the 3D data using afirst machine learning logic, applying a voxel classification to thedetected target objects using a second machine learning logic to providea segmentation of voxels depending on a material of the device therespective voxel represents, applying a transformation to feature spaceto the classified voxels, and obtaining measurement results based on thetransformation to feature space.
 2. The method of claim 1, wherein thedevice is a semiconductor device.
 3. The method of claim 2, wherein thetarget objects are interconnects between chip dies.
 4. The method ofclaim 1, wherein the first machine learning logic comprises a houghforest model.
 5. The method of claim 1, wherein the second machinelearning logic comprises a 3D random forest segmentation model.
 6. Themethod of claim 1, wherein the transformation to feature space includesa transformation to linear feature space.
 7. The method of claim 1,wherein the transformation to feature space comprises providing one ormore functions describing a dependency of a first dimensional variableto a second dimensional variable, or derivatives thereof.
 8. The methodof claim 7, wherein the first dimensional variable includes an area or adiameter.
 9. The method of claim 7, wherein the second dimensionalvariable includes a position variable.
 10. The method of claim 7,wherein obtaining measurements includes identifying deviations of thefunctions from nominal functions.
 11. The method of claim 7, wherein theone or more functions are user configurable.
 12. The method of claim 11,furthermore comprising predicting a desired user configuration.
 13. Anevaluation device for evaluating 3D data of a device under inspection,comprising one or more processors configured to: detect target objectsin the 3D data using a first machine learning logic, apply a voxelclassification to the detected target objects using a second machinelearning logic to provide a segmentation of voxels depending on materialof the device the respective voxel represents, apply a transformation tofeature space to the classified voxels, and obtain measurement resultsbased on the transformation to feature space.
 14. The evaluation deviceof claim 13, wherein the evaluation device is configured to perform themethod of claim
 1. 15. A system, comprising: a measurement deviceconfigured to obtain 3D data of a device under test, and the evaluationdevice of claim
 13. 16. A method for training the evaluation device ofclaim 13, comprising: training the first machine learning logic based ontraining data with annotated target objects, and training the secondmachine learning logic with training data including annotated voxels.17. A computer program including a program code which, when executed ona processor, causes execution of the method of claim
 1. 18. A tangiblenon-transitory storage medium having the computer program of claim 17stored thereon.
 19. The evaluation device of claim 13, wherein the firstmachine learning logic comprises a hough forest model.
 20. Theevaluation device of claim 13, wherein the second machine learning logiccomprises a 3D random forest segmentation model.